Información implícita y explícita en la percepción del covid-19 en los medios de comunicación social en español, alemán y ruso

  1. Pilgun, María 1
  2. Koreneva Antonova, Olga
  1. 1 Institute of Linguistics, Russian Academy of Sciences; Russian State Social University
Journal:
Palabra Clave

ISSN: 2027-534X 0122-8285

Year of publication: 2022

Issue Title: The Impact of COVID-19 on Communication. Analysis and Retrospective of the Effects of the Pandemic on the Media Ecosystem; e2511

Volume: 25

Issue: 1

Type: Article

DOI: 10.5294/PACLA.2022.25.1.3 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: Palabra Clave

Abstract

The COVID-19 pandemic radically changed people’s lives and the state of affairs in all spheres of life and transformed environmental ideas, social problems at the micro and macroeconomic levels, and the market mechanism to maintain economic justice. The pandemic consequences have exacerbated individualism, intersectionality, diversity, and inclusiveness issues. Disproportionate risks and worsened outlooks have been observed for socially and economically vulnerable groups. The present cross-cultural study discusses the content of social media on the COVID-19 perception by Spanish, German, and Russian-speaking actors, applying a multimodal approach and using neural network technologies and text analyses. The data analysis made it possible to identify common and distinctive features of communicative actors’ perception of various aspects of the COVID-19 pandemic. With the identity of explicitly expressed issues, the implicit information for the three types of users was significantly different, reflected in the dissimilar course and evolution of the COVID-19 pandemic around the world, shedding light on their cultural and political reasons.

Bibliographic References

  • Aaron M., A., Garrett N., Panayiota P.-Z., Talmina, N. y Linzen T. (2020). Cross-linguistic syntactic evaluation of word prediction models. arXiv:2005.00187 [cs.CL]
  • Agrati, L. S. et al. (2021). Bridges and mediation in higher distance education. Second International Workshop, HELMeTO 2020, Bari, BA, Italy, 17-18 de septiembre de 2020. Revised Selected Papers. Springer International. DOI: https://doi.org/10.1007/978-3-030-67435-9
  • Brandon, P., Traylor, A. y Pater, J. (2019). Learning reduplication with a neural network without explicit variables. University of Massachusetts Amherst. Sin publicar. http://works.bepress.com/joe_pater/38/
  • Casero-Ripollés, A. (2020). Impact of Covid-19 on the media system. Communicative and democratic consequences of news consumption during the outbreak. El Profesional de la Información, 29(2), e290223. DOI: https://doi.org/10.3145/epi.2020.mar.23
  • Clarke, N. y Furnell, S. (eds.) (2020). Human aspects of information security and assurance. 14th IFIP WG 11.12 International Symposium, HAISA 2020, Mytilene, Lesbos, Greece, 8-10 de julio de 2020, Proceedings. Springer International. DOI: https://doi.org/10.1007/978-3-030-57404-8
  • Essam, B. A. y Abdo, M.S. (2020) How do Arab tweeters perceive the Covid-19 pandemic? Journal of Psycholinguistic Research, 50, 507-521. DOI: https://doi.org/10.1007/s10936-020-09715-6
  • Garrett, L. (2001). Understanding media’s response to epidemics. Public Health Reports, 116(2), 87. DOI: https://doi.org/10.1016/S0033-3549(04)50149-8
  • Glik, D. (2007). Risk communication for public health emergencies. Annual Review of Public Health, 28, 33-54. DOI: https://doi.org/10.1146/annurev.publhealth.28.021406.144123
  • Gouri, S. B. y Pravat, K. S. (2021). GeoComputation and public health. A spatial approach. Springer. DOI: https://doi.org/10.1007/978-3-030-71198-6
  • Goyal, M.K. y Gupta, A.K. (2020). Integrated Risk of Pandemic: Covid-19 Impacts, Resilience and Recommendations. Springer.
  • Heydari, S. T. et al. (2021). The effect of risk communication on preventive and protective behaviors during the Covid-19 outbreak: mediating role of risk perception. BMC Public Health 21(54). DOI: https://doi.org/10.1186/s12889-020-10125-5
  • Hobbs, A. (2020). Leadership during Covid-19: Conflict and collaboration. Sage. DOI: https://doi.org/10.4135/9781529752021
  • Horesh, D. y Brown, A. D. (2020). Traumatic stress in the age of Covid-19: A call to close critical gaps and adapt to new realities. Psychological Trauma, 12(4), 331-335. DOI: https://doi.org/10.1037/tra0000592
  • Kharlamov, A. A. y Pilgun, M. (2020). Neuroinformatics and semantic representations. Cambridge Scholars Publishing.
  • Liao, H. P. y Wang, J. L. (2021). The impact of epidemic information on the public’s worries and attitude toward epidemic prevention measures during the Covid-19 outbreak. Current Psychology. DOI: https://doi.org/10.1007/s12144-021-01364-9
  • Linzen, T. (2019). What can linguistics and deep learning contribute to each other? Response to Pater. Language, 95(1), e98-e108. DOI: https://doi.org/10.1353/lan.2019.0015
  • Martín-Moreno, J. M. et al. (2020). Reflexiones sobre cómo evaluar y mejorar la respuesta a la pandemia de covid-19. Gaceta Sanitaria. DOI: https://doi.org/10.1016/j.gaceta.2020.11.008
  • McCoy, R. T., Grant, E., Smolensky, P., Griffiths, T. L. y Linzen, T. (2020). Universal linguistic inductive biases via meta-learning. arXiv:2006.16324 [cs.CL]
  • OMS - Organización Mundial de la Salud (2021). Global research on coronavirus disease (Covid-19). WHO. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-nco
  • Pater, J. (2019). Generative linguistics and neural networks at 60: foundation, friction, and fusion. Language, 95(1), e41-e7. DOI: https://doi.org/10.1353/lan.2019.0009
  • Prasad, A. (2020). The organization of ideological discourse in times of unexpected crisis: Explaining how Covid-19 is exploited by populist leaders. Leadership, 16(3), 294-302. DOI: https://doi.org/10.1177/1742715020926783
  • Quick, L. (2020). Managing events: Real challenges, real outcomes. Sage.
  • Roundtree, A. K. (2018). Hospital tweets on H1N1 and death panels: Text mining the situational crisis communication response to health crises and controversies. Kome, 6(1), 32-62. DOI: https://doi.org/10.17646/KOME.2018.13
  • Singh, L. et al. (2020). A first look at Covid-19 information and misinformation sharing on Twitter. arXiv:2003.13907v1 [cs.SI]. https://arxiv.org/pdf/2003.13907.pdf
  • Stefanidis, A. et al. (2017). Zika in Twitter: Temporal variations of locations, actors, and concepts. JMIR Public Health and Surveillance, 3(2), e22. DOI: https://doi.org/10.2196/publichealth.6925
  • Wang, P-W. et al. (2021). Sources of Covid-19-related information in people with various levels of risk perception and preventive behaviors in Taiwan: A latent profile analysis. International Journal of Environmental Research and Public Health, 18(4), 2091. DOI: https://doi.org/10.3390/ijerph18042091
  • Wasdani, K. P. y Prasad, A. (2020). The impossibility of social distancing among the urban poor: the case of an Indian slum in the times of Covid-19. Local Environment, 25(5), 414-418. DOI: https://doi.org/10.1080/13549839.2020.1754375
  • Wenham, C., Smith, J. y Morgan, R. (2020). Covid-19: The gendered impacts of the outbreak. The Lancet, 395(10227), 846-848. DOI: https://doi.org/10.1016/S0140-6736(20)30526-2
  • Xifra, J. (2020). Comunicación corporativa, relaciones públicas y gestión del riesgo reputacional en tiempos del covid-19. El Profesional de la Información, 29(2), e290220. DOI: https://doi.org/10.3145/epi.2020.mar.20